Big Data and Data Management

Welcome to the big data and data management community on BrightTALK. Join thousands of data quality engineers, data scientists, database administrators and other professionals to find more information about the hottest topics affecting your data. Subscribe now to learn about efficiently storing, optimizing a complex infrastructure, developing governing policies, ensuring data quality and analyzing data to make better informed decisions. Join the conversation by watching live and on-demand webinars and take the opportunity to interact with top experts and thought leaders in the field.

Don’t be naive when it comes to automated machine learning. Despite the unprecedented speed and ease of creating automated predictive models today, the human mind is still essential for generating good models.

From selecting the right problem to solve to preventing algorithm bias, machine learning is still an art and a science. To reap the benefits of automated machine learning, Jen Underwood, Founder of Impact Analytix, will share the most common mistakes – and battle-proven practices – to help you build better models.

Today's most successful companies are data-driven, making informed decisions instead of guesses. Traditionally data-driven insights have been delivered in the form of standalone business intelligence applications, but in the past few years we’ve seen a big shift towards delivering contextual analytics embedded directly into other software applications and business workflows. For software vendors this means meeting high end-user expectations by infusing compelling data visualizations and analytics into every application you create and sell. Modern data visualization and analytics applications, therefore, need to be designed from the ground-up to be quick and easy to embed for a web and mobile-first world. They also must capable of handling today's rapidly evolving modern data platforms so that your analytics technology does not lock you into using yesterday's
data stores.

If you're a CTO, product manager, or software engineering leader, you may be curious about whether to build or buy an integrated analytics solution, how to evaluate the available offerings for embedded analytics, and how to most efficiently embed data visualization and analytics into your application. In this webcast, we will cover the following topics:

The General Data Protection Regulation (GDPR) requires a strong information governance (IG) foundation to enable organizations to identify where personal data exists and the risks associated with it. Achieving compliance is about effectively setting, managing and auditing data related policies and processes – how a business collects and uses data. Strong data governance will be key as will ensuring that policies and rules are enforced and monitored.

This session will focus on how Information Governance and Stewardship are critical for ongoing GDPR compliance and addresses key areas such as article 16, article 32 and more.

Join this webinar to learn more about how to:
- Enforce governance policies through business processes
- Automatically apply and enforce your data quality processes and controls on all data in all systems in your organization
- Apply predictive processing and a guided experience through machine learning
- Improve data quality as required by article 16 immediate remediation, and ensure rule bindings associated with personal data have a high priority.

About the presenter:
Scott Wenzel has over 25 years of technology experience, helping companies integrate, manage, and improve the quality of their data. Scott specializes in Data Governance, Data Integration and Data Quality applications on multiple database and BI platforms and has extensive experience in Regulated Industries, Public Sector, Healthcare, Insurance, and Utility industries. He has spoken at numerous seminars and conferences (SAP SAPPHIRE, ASUG and others) on these and other related topics.

Machine Learning has become a competitive differentiator in a big data world. Vast amounts of data are already overwhelming existing BI tools and analytics processes. When faced with hundreds of variables, a human’s ability to efficiently identify new insights or detect changing patterns manually has also been exceeded. To address these challenges, BI and analytics professionals are adopting user-friendly, automated machine learning solutions.

In this on-demand webinar, recognized analytics industry expert Jen Underwood discusses how BI and analytics professionals can get started with automated machine learning.

–Identify the lines of code and the stages that cause performance issues related to CPU, memory, garbage collection, network, and disk I/O

–Easily disambiguate resources used during parallel stages

–Understand why run-time variations occur for the same application

–Determine whether performance issues are due to the application or other workloads on the cluster

–Receive actionable recommendations for tuning jobs

–Validate tuning changes made to applications with a before and after comparison

–View the highlights worst performing phases of jobs

–Improve MapReduce and Spark developer productivity

–Improve cluster efficiency based on clear recommendations on how to modify workloads and configurations

Vinod Nair leads product management at Pepperdata. He brings more than 20 years of experience in engineering and product management to the job, with a special interest in distributed systems and Hadoop. He has worked in software for telecommunications, financial management for small business, and big data. Vinod’s approach to product management is deeply influenced by his success in applying Lean Startup principles and rapid iteration to product design and development.

Incorporate data into your product, and take advantage of word class databases and a modern data platform. And learn from companies that have been there.

Join this webinar and learn:

- How to incorporate data into your product, and take advantage of word class databases and a modern data platform. And hear from companies that have been there.
- Learn how PDX improved their in house analytical solution and offered their pharmacies a premium level of service — granting them access to collaborate and share the data that they need.
- Learn how Namely, a HR platform, was able to leverage modern technologies ---databases optimized for performance, ETL tools, BI platforms that offer customization and can scale — to build out a scalable go to market solution in 3 months.

In this webinar, Greg Michaelson, PhD, and Head of DataRobot Labs, reviews the practical first steps an organization takes towards becoming an AI-driven enterprise and remaining competitive in the coming years.

Regardless of your geographical location, if you handle EU residents’ personal data, you also need to provide mechanisms that allow an individual to exercise the rights that are afforded by the GDPR. The ability to search, discover and review and delete data is a critical component of GDPR compliance.
But if that data is stored in multiple systems, and potentially shared with multiple partners, the tasks become dramatically more complex – requiring the technological ability to find and address all affected data promptly.
All of these rights require a new level of enterprise-wide data mapping, data governance, data architecture and system management. Join Primitive Logic’s technology solution experts in a conversation about how to address Subject Access Requests from the technology perspective.

Day 2 of 3 day series:
Day 2 - Focus on Privacy side of GDPR from a technology perspective

~ Day 1 – Overview of key concepts with legal and technical discussion of suggested actions
~ Day 3 – Focus on Security side of GDPR from a technology perspective

Do you think that your organization isn’t subject to the requirements of Europe’s impending General Data Protection Regulation (GDPR)? You may be wrong about that. If it is, are you on target to be compliant by May 25? This CLE-approved* webcast session will cover how data privacy requirements have evolved over time, the parameters associated with the GDPR, what they mean to your organization and what steps your organization needs to take to ensure compliance with the GDPR. Topics include:

+ How Data Privacy Requirements Have Evolved in the US and Europe
+ Scope of the GDPR Beyond the EU
+ A Definitional Baseline for GDPR
+ Important Changes and Organizational Impact
+ Data Existence and GDPR Compliance
+ Challenges Presented by Privacy Rights Associated with the GDPR
+ Fines: The Potential Cost of Non-Compliance
+ Business of the GDPR: Controllers and Processors
+ Steps to Take to Comply with the GDPR

Presentation Leader: Doug Austin

Doug is the VP of Products and Professional Services for CloudNine. At CloudNine, Doug manages professional services consulting projects for CloudNine clients. Doug has over 25 years of experience providing legal technology consulting, technical project management and software development services to numerous commercial and government clients.

Special Consultant to CloudNine: Tom O'Connor

Tom O’Connor is a nationally known consultant, speaker, and writer in the field of computerized litigation support systems. Tom’s consulting experience is primarily in complex litigation matters.

Predicting customer behavior is quite challenging. However, knowledge of a customer at an individual level offers enormous benefits. A company with such knowledge can provide better customer experience and retention, targeted marketing, increase sales, and proactive care. But many existing customer models are very macro in nature and fail to deliver at an individual level.

Machine Learning allows us to build such micro level models by taking into account all digital touch points of a customer with the company. These models will help predict what a customer is going to do next, what their near-future behavior will be and what response to anticipate from an action.

On this webinar, you’ll hear the current state of machine learning in Customer 360 – and then learn how you can stay one step ahead in building highly customer-centric models for your business.

In order to run successful machine learning projects, and create highly-accurate predictive models for your business, you need effective data preparation. Although machine learning automation provides safeguards to prevent common mistakes, you’ll still want to correctly prepare, shape and format your data to generate optimal models.

In this on-demand webinar, Jen Underwood, Founder of Impact Analytix reviews how to organize data in a machine learning-friendly format that accurately reflects the business process and outcomes. She shares basic guidelines, practical tips, and additional resources to help get you started mastering the essence of predictive model data preparation.

During this demo you’ll get a firsthand look at Vertica machine learning using a public IoT dataset from the Irish Smart Meter project. The demonstration will cover data exploration and visualization, data preparation, model building, model scoring, model evaluation, and model management. Learn how data scientists and analysts can leverage Vertica to embrace the power of Big Data and accelerate business outcomes with no limits and no compromises.

In this webinar, you’ll learn:
•Key trends, challenges and opportunities in the manufacturing industry based on PAC research.
•How Hitachi addresses digital transformation with machine learning and advanced analytics.
•Top use cases from global manufacturers who see strong outcomes and high return on investment with IoT.

Compliance organizations within banks and other financial institutions are turning to machine learning for improving their AML compliance programs. Today, the systems that aim to detect potentially suspicious activity are commonly rule-based, and suffer from ultra-high false positive rates. Automated machine learning provides a solution to address this challenge.

In this webinar, Dan Yelle, a Customer-Facing Data Scientist for DataRobot will show how automated machine learning can be used to reduce false positive rates, thereby improving the efficiency of AML transaction monitoring and reducing costs.

Preventing fraud is a mission-critical objective of every financial institution, including fintechs. But those committing fraud continue to evolve their tactics to evade detection by even the best prepared organizations.

On this on-demand webinar, you’ll get an overview of the current state of machine learning in fraud detection – and learn how you can stay one step ahead of those looking to harm your business.

Machine learning has quickly become the tool of choice for pricing a variety of financial products. Instead of utilizing legacy rules-based matrices for pricing, companies have turned to predictive modeling to understand the likelihood of default and overall borrower repayment performance. This has enabled companies to move from older pricing schemes to dynamic risk-based pricing.

Justin Dickerson, General Manager of Global Fintech for DataRobot and Igor Veksler, a leading Customer-Facing Data Scientist for DataRobot have both worked in the alternative finance industry as data scientists and led this transition to risk-based pricing for their respective organizations. As leaders at DataRobot, they currently share their expertise with clients and potential customers looking to leverage machine learning to make the transition to dynamic risk-based pricing.

In this on-demand webinar, Justin and Igor describe how DataRobot can help enable your enterprise to leverage automated machine learning to become a leader in risk-based pricing.

In this talk we will see whether we are building our first product or revamping an existing one, Embedded Analytics can help us solve real customer problems, which builds product value and creates a competitive differentiator to propel our business forward.

Additionally, we'll deeply look into how Embedded Analytics is different from Traditional Business Intelligence and what are the factors/trends driving Embedded Analytics.

As the volume and complexity of data continue to grow, leading organizations across every industry have one thing in common — they view data as a strategic asset. What’s more, they have figured out how to break down their organization’s data silos, using analytics to turn data into game-changing insights that help transform their businesses.

So how are they accomplishing this?

Join David Bolton, VP of Industry Solutions, and Chris Mabardy, Sr Director of Product Marketing, as they share how some of the world’s largest and most successful organizations have leveraged Qlik to deliver real business value. See how Qlik’s industry leading Associative Engine allows them to:

Combine different data sources without complex modeling
Explore, search, and pivot their data in any direction
Seamlessly handle both big and small data
Take the first step to delivering business value from your data in 2018.

Mike shares insights from a research study sponsored by Actian on the need and adoption of hybrid data.
Mike Hoskins shares the steps Actian has taken to enable the hybrid enterprise to perform real-time analytics.

Thinking about implementing object storage in your infrastructure and not sure what hardware you need? With information and tips curated from hundreds of installations, VP of Product Tony Barbagallo and Sr. Consultant John Bell will explain the criteria you should consider as you make your selections and will be available to answer your questions.

Data visualization requires data to be prepared before any meaningful analysis can be conducted. Finding insights, making correct observations and taking actions to drive outcomes therefore don't just depend on the way information is communicated but also on the preparation preceding the analysis.

In this webinar we discuss the key steps for data preparation to enable effective analysis and visual exploration of the data. We will show practical examples from projects we have worked on as well as share some simple data preparation ideas from our Makeover Monday challenges.

Lastly, we will show an example of how data preparation can enrich a dataset and enable further analysis.

2018 is the year for containers. IT teams are using them to bring new levels of cloud portability, infrastructure utilization, and micro-services to applications. There’s just one big catch: what about the data? How will you bring container-style automation, portability, and operations to enterprise applications and data? Attend this webinar and learn how to make it easy. HPE will share top use cases and show a demo of advanced data services with persistent storage for teams.

The future is here. We have entered a new era of analytics. Data volumes and complexity have exceeded the limits of current manual drag-and-drop solutions. Data is moving at the speed of business but speed-to-insight lags behind. It is time to adopt intelligent next generation, machine-powered analytics interacting with human context to retain your competitive edge.

Given the recent demand for data analytics and data science skills, adequately testing and qualifying candidates can be a daunting task. Interviewing hundreds of individuals of varying experience and skill levels requires a standardized approach. In this webinar, Tanya Cashorali explores strategies, best practices, and deceptively simple interviewing techniques for data analytics and data science candidates.

With the ServiceNow’s new Kingston release, fans will not only get new functionality for a richer user experience but enjoy important strides in perhaps the most anticipated areas area of the day: machine learning and automation.Every release brings us more expansion across the ServiceNow Platform, and Kingston is no exception, adding new features and functionality including advances in the ServiceNow Platform, CSM, HR Service Delivery, ITOM, ITBM, Performance Analytics, Security Operations, GRC, and, of course, IT Service Management (including Software Asset Management). Taken in context, these new capabilities make a strong case that ServiceNow is setting itself up to act as the technology backbone for modern organizations (so keep an eye out for future platform plays!).

AI is changing the way organizations do businesses and how they interact with customers. AI continues to drive the change. Deep Learning and Natural Language Processing will become standards in AI solutions. Deep Learning is based on brain simulations and uses deep neural networks. AlphaGo is the first AI system to defeat a professional human Go player, the first program to defeat a Go world champion, and arguably the strongest Go player in history. Baidu improved speech recognition from 89% to 99% using Deep Learning. Every AI and Machine learning scientist is required to know Deep Learning tools in his / her current job scenario.

In this session, we will be discussing what is Deep Learning and why it is gaining popularity. We will explain AI solutions using Deep Learning with a practical example. Deep Learning has an edge over other machine learning techniques as with the increased volume of data, performance increases with Deep Learning. Further, Deep Learning enables Hierarchical Feature Learning i.e. learning feature hierarchies.

Watson is a computer system capable of answering questions posed in natural language. Watson was named after IBM's first CEO, Thomas J. Watson. The computer system was specifically developed to answer questions on the quiz show Jeopardy! (where it beat its human competitors) and was then used in commercial applications, the first of which was helping with lung cancer treatment.

NetApp is now using IBM Watson in Elio, a virtual support assistant that responds to queries in natural language. Elio is built using Watson’s cognitive computing capabilities. These enable Elio to analyze unstructured data by using natural language processing to understand grammar and context, understand complex questions, and evaluate all possible meanings to determine what is being asked. Elio then reasons and identifies the best answers to questions with help from experts who monitor the quality of answers and continue to train Elio on more subjects.

Elio and Watson represent an innovative and novel use of large quantities of unstructured data to help solve problems, on average, four times faster than traditional methods. Join us at this webcast, where we’ll discuss:

You’re a CIO, CISO or DPO - and you’ve been woken up in the middle of the night because personal data held by your organization has been discovered for sale on the dark web. This disclosure puts the privacy of your customers at risk. What do you do next?

Join this session to learn about the impact of GDPR and go through a breach investigation and response scenario as it would be after GDPR comes into effect in May 2018.

What you will learn:
- What breach response will look like under the GDPR
- What tools and processes a data privacy officer will rely on in case of a breach
- What departments and entities will be involved beyond IT
- What activities are currently happening within organizations to prepare for the GDPR
- What the consequences of the breach could be

The world of data is changing at breakneck speed. Costs are dropping, speed is increasing, and centralization is getting easier. But getting more value from your data isn’t always easy. We’ll talk about the key trends in analytics and how to prepare your organization for this new world.

Join this webinar and learn:

- Which new analytic technologies and trends you need to be aware of and how they’ll impact your business;
- How to use new technologies to actually empower business users to self-serve to answers and make better decisions using data in their day-to-day workflows
- Pitfalls to avoid (and flashy technologies to ignore) when you’re planning your analytics strategy

Silicon valley venture capitalist Jake Flomenberg gets to see, track and make investments in the evolution of big picture technology trends across areas such as big data analytics, machine learning and artificial intelligence, and emerging modern data platforms and data types that are enabling organizations to be data and analytics-driven. Data driven companies make more effective information backed decisions and tend to significantly outcompete their peers operating on guesses and gut feel. Some companies are now even selling ‘data products’, for example aircraft engine manufacturers tracking and analyzing huge volumes of engine performance data to enable predictive maintenance, fixing things before failure to avoid costs and negative impact on customers.

Jake will discuss trends he’s seeing related to the data analytics market, such as the growing need for self-service data discovery on very large volumes of data, analytics on streams of data, analytics on unstructured data, and contextual analytics embedded inside of software vendor and enterprise applications. He’ll discuss the characteristics of these markets, where he sees these markets going in the future, and why his firm chose to invest in a company like Zoomdata.

Zoomdata CTO Ruhollah Farchtchi will then discuss industry trends he has forecast for 2018 including the eclipsing of relational databases by modern data platforms for doing analytics, how the cloud has changed the game for application development, and how working with streaming data is becoming the new normal. He’ll also discuss trends that are more specifically relevant to Zoomdata such as how to leverage the value of company’s investments in modern elastically scalable back-end data infrastructure and how to get corresponding value on the front-end, such as the ability to analyze and get insights from huge volumes of data, streaming data, and unstructured data types.

In this webinar Manfred Berger introduces the latest addition to the HGST platform portfolio, the SVR2U24 NVMe all-flash storage server.

Combining high performance NVMe SSDs and Intel®’s Purley server architecture into one tried and tested unit, the SVR2U24 enables customers to come to market quickly with a multitude of software defined storage solutions or data base servers optimized for high speed search operations, catering to a multitude of industry verticals.

The dirty little secret? The General Data Protection Regulation (GDPR) does not just affect companies in the EU, it affects companies around the world that collect or process data concerning EU residents. Caringo Product Manager Glen Olsen and Solutions Architect Alex Oldfield explain the challenges organisations face and how Caringo Swarm Object Storage provides a cost-effective solution to help meet the requirements as well as how Data Protection Officers can use Swarm to monitor and enable GDPR compliance.

Join this session to discover the key principles that differentiate data-aware or data-driven businesses from their insights-driven peers and competitors. The session will explore the roles that data virtualization (aka Data Fabric) plays in modern SOI architectures, such as:

- A single virtual catalog / view on all enterprise data sources including data lakes.
- A more agile and flexible virtual enterprise data warehouse.
- A common semantic layer for business intelligence (BI) and analytical applications (aka BI Fabric).

AI isn’t a nice-to-have any more, it’s a must-have. There’s a reason why corporate giants like Google, IBM, Yahoo, Intel, Apple, and Salesforce are competing to snatch up private AI companies. In the first quarter of 2017 alone, 37 AI companies were swallowed whole.

Companies aren’t just using these AI tools to enable existing marketing strategies; they’re taking those strategies to the next level, going beyond simple retention and delivering active engagement. AI technology offers companies unprecedented insight into customer behaviors, patterns, and beliefs, allowing you to seamlessly anticipate customer needs and serve up hyper-personalized, emotionally resonant campaigns where and when they’re most welcome.

2018 is the year to seize the AI advantage. To learn more about the technology you need, the opportunities it unlocks, and what it takes to get your ball in the game, don’t miss this VB Live event!

In this webinar, you'll learn:
* What’s new in AI for 2018 -- and what’s coming down the pike
* How businesses are using AI to drive results
* How to go beyond customer retention and power customer engagement

A practical way to get started with IIoT in a brownfield factory is by creating a “Green Patch in your Brownfield.”

Everyone is talking about deploying IIoT in brownfields to achieve incremental gains in productivity. But in the greenfield, IIoT can deliver a game changer: to change your business model to mass customization through adaptive machines. Batch size of one. Deliver direct from the production line to consumers. Flatten your supply chain, your sales chain. No greenfield in your near future? To get started, get focused. For your next capex, put a 'focused factory' in your existing plant -- a 'green patch' in your brownfield. Learn from a pilot project the full potential of IIoT with today's state of the art automation technologies, not the legacy assets of the brownfield.

For data scientists, predictive analytics is critical – but did you know it can also save lives? Crisis Text Line is using Periscope Data and Python to do just that – improving identification of data trends through natural language processing to better support people in crisis.

Join Periscope Data and Crisis Text Line Data Scientist, Scotty Huhn, for a webinar to learn more about powering predictive analytics with Python and Periscope Data. We’ll also share how, for the first time, Periscope Data is supporting SQL, Python, and R on one platform, enabling users to do more complex analysis in just a fraction of the time.